Inferring ground-level nitrogen dioxide concentrations at fine spatial resolution applied to the TROPOMI satellite instrument
Satellite-based estimates of ground-level nitrogen dioxide (NO _2 ) concentrations are useful for understanding links between air quality and health. A longstanding question has been why prior satellite-derived surface NO _2 concentrations are biased low with respect to ground-based measurements. In...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2020-01-01
|
| Series: | Environmental Research Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1748-9326/aba3a5 |
| _version_ | 1797747836490612736 |
|---|---|
| author | Matthew J Cooper Randall V Martin Chris A McLinden Jeffrey R Brook |
| author_facet | Matthew J Cooper Randall V Martin Chris A McLinden Jeffrey R Brook |
| author_sort | Matthew J Cooper |
| collection | DOAJ |
| description | Satellite-based estimates of ground-level nitrogen dioxide (NO _2 ) concentrations are useful for understanding links between air quality and health. A longstanding question has been why prior satellite-derived surface NO _2 concentrations are biased low with respect to ground-based measurements. In this work we demonstrate that these biases are due to both the coarse resolution of previous satellite NO _2 products and inaccuracies in vertical mixing assumptions used to convert satellite-observed tropospheric columns to surface concentrations. We develop an algorithm that now allows for different mixing assumptions to be used based on observed NO _2 conditions. We then apply this algorithm to observations from the TROPOMI satellite instrument, which has been providing NO _2 column observations at an unprecedented spatial resolution for over a year. This new product achieves estimates of ground-level NO _2 with greater accuracy and higher resolution compared to previous satellite-based estimates from OMI. These comparisons also show that TROPOMI-inferred surface NO _2 concentrations from our updated algorithm have higher correlation and lower bias than those found using TROPOMI and the prior algorithm. TROPOMI-inferred estimates of the population exposed to NO _2 conditions exceeding health standards are at least three times higher than for OMI-inferred estimates. These developments provide an exciting opportunity for air quality monitoring. |
| first_indexed | 2024-03-12T15:56:20Z |
| format | Article |
| id | doaj.art-0a13ad0c3e444d2c94368397e0319c09 |
| institution | Directory Open Access Journal |
| issn | 1748-9326 |
| language | English |
| last_indexed | 2024-03-12T15:56:20Z |
| publishDate | 2020-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Environmental Research Letters |
| spelling | doaj.art-0a13ad0c3e444d2c94368397e0319c092023-08-09T14:53:11ZengIOP PublishingEnvironmental Research Letters1748-93262020-01-01151010401310.1088/1748-9326/aba3a5Inferring ground-level nitrogen dioxide concentrations at fine spatial resolution applied to the TROPOMI satellite instrumentMatthew J Cooper0https://orcid.org/0000-0002-4145-3458Randall V Martin1https://orcid.org/0000-0003-2632-8402Chris A McLinden2https://orcid.org/0000-0001-5054-1380Jeffrey R Brook3Department of Physics and Atmospheric Science, Dalhousie University , Halifax, Nova Scotia, Canada; Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis , St. Louis, MO, United States of America; Author to whom any correspondence should be addressed.Department of Physics and Atmospheric Science, Dalhousie University , Halifax, Nova Scotia, Canada; Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis , St. Louis, MO, United States of America; Harvard-Smithsonian Center for Astrophysics , Cambridge, MA, United States of AmericaEnvironment and Climate Change Canada , Toronto, Ontario, CanadaDalla Lana School of Public Health, University of Toronto , Toronto, Ontario, CanadaSatellite-based estimates of ground-level nitrogen dioxide (NO _2 ) concentrations are useful for understanding links between air quality and health. A longstanding question has been why prior satellite-derived surface NO _2 concentrations are biased low with respect to ground-based measurements. In this work we demonstrate that these biases are due to both the coarse resolution of previous satellite NO _2 products and inaccuracies in vertical mixing assumptions used to convert satellite-observed tropospheric columns to surface concentrations. We develop an algorithm that now allows for different mixing assumptions to be used based on observed NO _2 conditions. We then apply this algorithm to observations from the TROPOMI satellite instrument, which has been providing NO _2 column observations at an unprecedented spatial resolution for over a year. This new product achieves estimates of ground-level NO _2 with greater accuracy and higher resolution compared to previous satellite-based estimates from OMI. These comparisons also show that TROPOMI-inferred surface NO _2 concentrations from our updated algorithm have higher correlation and lower bias than those found using TROPOMI and the prior algorithm. TROPOMI-inferred estimates of the population exposed to NO _2 conditions exceeding health standards are at least three times higher than for OMI-inferred estimates. These developments provide an exciting opportunity for air quality monitoring.https://doi.org/10.1088/1748-9326/aba3a5air qualityNO2TROPOMIremote sensing |
| spellingShingle | Matthew J Cooper Randall V Martin Chris A McLinden Jeffrey R Brook Inferring ground-level nitrogen dioxide concentrations at fine spatial resolution applied to the TROPOMI satellite instrument Environmental Research Letters air quality NO2 TROPOMI remote sensing |
| title | Inferring ground-level nitrogen dioxide concentrations at fine spatial resolution applied to the TROPOMI satellite instrument |
| title_full | Inferring ground-level nitrogen dioxide concentrations at fine spatial resolution applied to the TROPOMI satellite instrument |
| title_fullStr | Inferring ground-level nitrogen dioxide concentrations at fine spatial resolution applied to the TROPOMI satellite instrument |
| title_full_unstemmed | Inferring ground-level nitrogen dioxide concentrations at fine spatial resolution applied to the TROPOMI satellite instrument |
| title_short | Inferring ground-level nitrogen dioxide concentrations at fine spatial resolution applied to the TROPOMI satellite instrument |
| title_sort | inferring ground level nitrogen dioxide concentrations at fine spatial resolution applied to the tropomi satellite instrument |
| topic | air quality NO2 TROPOMI remote sensing |
| url | https://doi.org/10.1088/1748-9326/aba3a5 |
| work_keys_str_mv | AT matthewjcooper inferringgroundlevelnitrogendioxideconcentrationsatfinespatialresolutionappliedtothetropomisatelliteinstrument AT randallvmartin inferringgroundlevelnitrogendioxideconcentrationsatfinespatialresolutionappliedtothetropomisatelliteinstrument AT chrisamclinden inferringgroundlevelnitrogendioxideconcentrationsatfinespatialresolutionappliedtothetropomisatelliteinstrument AT jeffreyrbrook inferringgroundlevelnitrogendioxideconcentrationsatfinespatialresolutionappliedtothetropomisatelliteinstrument |